Object delineation by kappa-connected components

dc.creatorMiranda, PAV
dc.creatorFalcao, AX
dc.creatorRocha, A
dc.creatorBergo, FPG
dc.date2008
dc.date2014-11-13T15:22:17Z
dc.date2015-11-26T18:08:02Z
dc.date2014-11-13T15:22:17Z
dc.date2015-11-26T18:08:02Z
dc.date.accessioned2018-03-29T00:50:09Z
dc.date.available2018-03-29T00:50:09Z
dc.identifierEurasip Journal On Advances In Signal Processing. Hindawi Publishing Corporation, 2008.
dc.identifier1687-6172
dc.identifierWOS:000259469600001
dc.identifier10.1155/2008/467928
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/62289
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/62289
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/62289
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1293729
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionThe notion of "strength of connectedness" between pixels has been successfully used in image segmentation. We present extensions to these works, which can considerably improve the efficiency of object delineation tasks. A set of pixels is said to be a kappa-connected component with respect to a seed pixel, when the strength of connectedness of any pixel in that set with respect to the seed is higher than or equal to a threshold. We discuss two approaches that define objects based on kappa-connected components with respect to a given seed set: with and without competition among seeds. While the previous approaches either assume no competition with a single threshold for all seeds or eliminate the threshold for seed competition, we show that seeds with different thresholds can improve segmentation in both paradigms. We also propose automatic and user-friendly interactive methods to determining the thresholds. The proposed methods are presented in the framework of the image foresting transform, which naturally leads to efficient and correct graph algorithms. The improvements are demonstrated through several segmentation experiments involving medical images. Copyright (C) 2008 Paulo A. V. Miranda et al.
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFAPESP [05/59808-0, 05/58103-3, 05/56578-4, 03/13424-1]
dc.descriptionCNPq [302617/2007-8, 472402/2007-2]
dc.languageen
dc.publisherHindawi Publishing Corporation
dc.publisherNew York
dc.publisherEUA
dc.relationEurasip Journal On Advances In Signal Processing
dc.relationEURASIP J. Adv. Signal Process.
dc.rightsaberto
dc.sourceWeb of Science
dc.subjectRelative Fuzzy Connectedness
dc.subjectImage Foresting Transform
dc.subjectLive Wire
dc.subjectSegmentation
dc.subjectAlgorithms
dc.subjectDefinition
dc.subjectVolume
dc.subjectCuts
dc.titleObject delineation by kappa-connected components
dc.typeArtículos de revistas


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